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71 results found

tmmc-lnpy: A python package to analyze Transition Matrix Monte Carlo lnPi data.

Data provided by  National Institute of Standards and Technology

A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data.

Tags: python,molecular simulation,data analysis,Transition Matrix Monte Carlo,statistical mechanics,

Modified: 2024-02-22

Views: 0

FCpy: Feldman-Cousins Confidence Interval Calculator

Data provided by  National Institute of Standards and Technology

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,

Modified: 2024-02-22

Views: 0

Entropy Source Validation Client

Data provided by  National Institute of Standards and Technology

This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.

Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,

Modified: 2024-02-22

Views: 0

Supplemental Data and Source Code for ILSA Research

Data provided by  National Institute of Standards and Technology

Source code associated with the Inverted Library Search Algorithm (ILSA) for identifying mixture components using is-CID mass spectra. This source code is frozen to accompany the manuscript titled "Updates to the Inverted Library Search Algorithm for mixture analysis" by Moorthy et. al.

Tags: mass spectrometry,Mixture Analysis,Search Algorithms,seized drug analysis,

Modified: 2024-02-22

Views: 0

Python tools for measuring filament defects in embedded 3D printing

Data provided by  National Institute of Standards and Technology

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.

Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,

Modified: 2024-02-22

Views: 0

Simantha: Simulation for Manufacturing

Data provided by  National Institute of Standards and Technology

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

MAM Consortium Interlaboratory Study Raw Data

Data provided by  National Institute of Standards and Technology

These LC-MS and LC-MS/MS raw data were collected for purposes of an interlaboratory study evaluating the multi-attribute method (MAM). Tryptic digests of native NISTmAb (the "Reference" and the "Unknown" samples), degraded NISTmAb (the "pH Stress" sample) and NISTmAb spiked with 15 heavy-labeled synthetic peptides (the "Spike" sample) were sent to each participating laboratory. One injection of each digest was acquired in MS-only mode, while a second injection was acquired in MS/MS mode.

Tags: attribute analytics,interlaboratory study,multi-attribute method,MAM Consortium,mass spectrometry,new peak detection,NPD,purity testing,round robin,targeted analytics,NISTmAb,

Modified: 2024-02-22

Views: 0

multicomplex: C++ and Python code for multicomplex arithmetic

Data provided by  National Institute of Standards and Technology

The library multicomplex is an implementation of multicomplex algebra in C++ to allow for higher-order derivatives of numerical functions. Many (though not all) mathematical functions are implemented, allowing for calculation of derivatives (straight and mixed) to approximately numerical precision, which is difficult or impossible to achieve in conventional double precision

Tags: mathematics,multicomplex,derivatives,C++,python,

Modified: 2024-02-22

Views: 0

The NIST Scan Framework for ARTIQ

Data provided by  National Institute of Standards and Technology

The NIST scan framework is a framework that greatly simplifies the process of writing and maintaining scans of experimental parameters using the ARTIQ control system and language. The framework adopts the philosophy of convention over configuration where datasets are stored for analysis and plotting in a standard directory structure. The framework provides a number of useful features such as automatic calculation of statistics, fitting, validation of fits, and plotting that do not need to be performed by the user.

Tags: ARTIQ,python,Scans,

Modified: 2024-02-22

Views: 0

SRM 2969 Vitamin D Metabolites in Frozen Human Serum (Total 25-Hydroxyvitamin D Low Level) Data

Data provided by  National Institute of Standards and Technology

SRM 2969 Electronic files containing certified values and their uncertainties, and the data used to assign those values

Tags: vitamin D,25-hydroxyvitamin D,serum,mass spectrometry,

Modified: 2024-02-22

Views: 0